How do guideline developers identify, incorporate and report patient preferences? An international cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: Guidelines based on patient preferences differ from those developed solely by clinicians and may promote patient adherence to guideline recommendations. There is scant evidence on how to develop patient-informed guidelines. This study aimed to describe how guideline developers identify, incorporate and report patient preferences. METHODS: We employed a descriptive cross-sectional survey design. Eligible organizations were non-profit agencies who developed at least one guideline in the past five years and had considered patient preferences in guideline development. We identified developers through the Guidelines International Network and publicly-available guideline repositories, administered the survey online, and used summary statistics to report results. RESULTS: The response rate was 18.3% (52/284). Respondents included professional societies, and government, academic, charitable and healthcare delivery organizations from 18 countries with at least 1 to ≥6 years of experience generating patient-informed guidelines. Organizations most frequently identified preferences through patient panelists (86.5%) and published research (84.6%). Most organizations (48, 92.3%) used multiple approaches to identify preferences (median 3, range 1 to 5). Most often, organizations used preferences to generate recommendations (82.7%) or establish guideline questions (73.1%). Few organizations explicitly reported preferences; instead, they implicitly embedded preferences in guideline recommendations (82.7%), questions (73.1%), or point-of-care communication tools (61.5%). Most developers had little capacity to generate patient-informed guidelines. Few offered training to patients (30.8%), or had dedicated funding (28.9%), managers (9.6%) or staff (9.6%). Respondents identified numerous barriers to identifying preferences. They also identified processes, resources and clinician- and patient-strategies that can facilitate the development of patient-informed guidelines. In contrast to identifying preferences, developers noted few approaches for, or barriers or facilitators of incorporating or reporting preferences. CONCLUSIONS: Developers emphasized the need for knowledge on how to identify, incorporate and report patient preferences in guidelines. In particular, how to use patient preferences to formulate recommendations, and transparently report patient preferences and the influence of preferences on guidelines is unknown. Still, insights from responding developers may help others who may be struggling to generate guidelines informed by patient preferences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".